code: def test_conv2d_relu(self, device):
      # we generate base sample
      target = torch.LongTensor(1, 6, 5).fill_(0).to(device)
      for i in range(1, self.num_classes):
          target[..., i:-i, i:-i] = i
  
      m = nn.Sequential(
          nn.Conv2d(1, self.num_classes // 2, kernel_size=3, padding=1),
          nn.ReLU(True),
          nn.Conv2d(
              self.num_classes // 2,
              self.num_classes,
              kernel_size=3,
              padding=1,
          ),
      ).to(device)
      m.apply(self.init_weights)
  
      optimizer = optim.Adam(m.parameters(), lr=self.lr)
  
      criterion = kornia.losses.FocalLoss(
          alpha=self.alpha, gamma=self.gamma, reduction="mean"
      )
      # NOTE: uncomment to compare against vanilla cross entropy
      # criterion = nn.CrossEntropyLoss()
  
      for _ in range(self.num_iterations):
          sample = self.generate_sample(target).to(device)
          output = m(sample)
          loss = criterion(output, target.to(device))
          logger.debug(f"Loss: {loss.item()}")
  
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
      sample = self.generate_sample(target).to(device)
      output_argmax = torch.argmax(m(sample), dim=1)
      logger.debug(f"Output argmax: \n{output_argmax}")
  
      # TODO(edgar): replace by IoU or find a more stable solution
      #              for this test. The issue is that depending on
      #              the seed to initialize the weights affects the
      #              final results and slows down the convergence of
      #              the algorithm.
      val = F.mse_loss(output_argmax.float(), target.float())
      if not val.item() < self.thresh:
          pytest.xfail("Wrong seed or initial weight values.")
  
file_path: kornia/test/integration/test_focal.py
imports: import logging
  import pytest
  import torch
  import torch.nn.functional
  from torch import nn, optim
  import kornia
label_desc: Use image node_id 3 for calling the TestIntegrationFocalLoss obj's underlying member method code with example usage: obj.test_conv2d_relu(device) without return types